• Vephla
  • Data

Data Science

Unlock the power of data with our comprehensive "Data Science" course! Designed for beginners as well as those looking to enhance their skills, this course offers a deep dive into the essential concepts and techniques that form the foundation of the data science field.

Learn: MySQL | SQL | CTEs | EDA | Python | R | Reinforcement Learning | Deep Learning

  • 4.9

  • 100% Live Lessons

  • 1, 000+ Students

Mentor: Dr Richards Michael

What you'll learn

    Module 1: Introduction to Data Science

  • Overview of Data Science: Definition, importance, and real-world applications
  • Data Science workflow: Data collection, cleaning, analysis, visualization, and interpretation
  • Key tools in Data Science: Python, R, SQL, and more

    • Module 2: Statistics and Probability

  • Descriptive statistics: Mean, median, mode, variance, and standard deviation
  • Inferential statistics: Hypothesis testing, confidence intervals
  • Probability theory: Concepts, distributions, and theorems
  • Random variables: Discrete and continuous distributions
  • Bayes’ Theorem

    • Module 3: Basics of Machine Learning

  • Introduction to Machine Learning: Supervised, unsupervised, and reinforcement learning
  • Overview of Machine Learning algorithms: Classification, regression, clustering, and more
  • Model training, validation, and testing
  • Bias-variance tradeoff
  • Overfitting and underfitting

    • Module 4: Linear Regression

  • Simple and multiple linear regression
  • Assumptions of linear regression
  • Evaluating regression models: R-squared, Mean Squared Error (MSE), etc.
  • Feature scaling and normalization

    • Module 5: Logistic Regression

  • Introduction to logistic regression for binary classification
  • Sigmoid function and probability interpretation
  • Maximum likelihood estimation
  • Evaluating model performance: Confusion matrix, precision, recall, F1-score, AUC-ROC curve

    • Module 6: Decision Trees

  • Understanding decision tree structure: Nodes, branches, leaves
  • Splitting criteria: Gini index, entropy, and information gain
  • Pruning techniques to prevent overfitting
  • Advantages and limitations of decision trees

    • Module 7: Random Forest

  • Introduction to ensemble methods
  • How Random Forest works: Bagging and boosting
  • Feature importance in Random Forest
  • Hyperparameter tuning for Random Forest

    Module 8: K-Nearest Neighbors (KNN)

  • Distance metrics: Euclidean, Manhattan, and others
  • Choosing the right K value
  • Strengths and limitations of KNN
  • Applications of KNN in real-world scenarios

    • Module 9: Naive Bayes

  • Bayes’ Theorem and its application in classification
  • Assumptions of Naive Bayes
  • Types of Naive Bayes classifiers: Gaussian, Multinomial, Bernoulli
  • Use cases of Naive Bayes in spam filtering, sentiment analysis, etc.

    • Module 10: Support Vector Machine (SVM)

  • Concept of hyperplanes and support vectors
  • Linear and non-linear SVM
  • Kernel functions: Polynomial, RBF, etc. Soft margin vs hard margin classification

    • Module 11: K-Means Clustering

  • Introduction to clustering techniques
  • K-Means algorithm: Centroid calculation and cluster assignment
  • Choosing the optimal number of clusters with the Elbow method
  • Limitations and applications of K-Means

    • Module 12: Association Rule Mining

  • Market basket analysis and association rules
  • Apriori algorithm for association rule learning
  • Support, confidence, and lift
  • Applications of association rules in retail and e-commerce

    • Module 13: Reinforcement Learning

  • Basic concepts of reinforcement learning: Agents, states, actions, rewards
  • Exploration vs exploitation dilemma
  • Markov Decision Processes (MDPs)
  • Q-learning and policy-based methods

    • Module 14: Deep Learning

  • Introduction to neural networks and deep learning
  • Types of neural networks: Feedforward, Convolutional (CNN), Recurrent (RNN)
  • Backpropagation and gradient descent
  • Activation functions: Sigmoid, ReLU, Softmax
  • Introduction to frameworks like TensorFlow and PyTorch

    • Module 15: Data Science Interview Questions

  • Common interview questions on data science concepts, algorithms, and techniques
  • Hands-on exercises to practice problem-solving and coding
  • Tips for technical interviews: How to approach machine learning problems, discuss models, and optimize solutions
  • Behavioral interview preparation: Communicating projects, teamwork, and challenges faced

Requirements

  • This course has no skill prerequisites; however, having a basic familiarity with computer operations is beneficial.
  • Personal computer—whether it's a Mac, Windows PC, or a Linux machine
  • A stable internet connection is essential for engaging in virtual classes, downloading required softwares, and for individual practice.
  • Time

About This Course

Throughout this course, you will explore the complete data science lifecycle, from data collection to the communication of insights. Key topics include:

  • Data Collection: Learn how to gather data from various sources, including APIs and web scraping techniques.
  • Data Cleaning and Preparation: Understand the importance of preprocessing data, handling missing values, and ensuring data quality.
  • Exploratory Data Analysis (EDA): Discover how to visualize data and identify patterns, trends, and anomalies that inform decision-making.
  • Statistical Analysis: Gain insights through statistical methods, including regression analysis and hypothesis testing.
  • Machine Learning Fundamentals: Introduce yourself to the world of machine learning, exploring supervised and unsupervised learning techniques and how to build predictive models.
  • Data Visualization: Master the art of storytelling with data through compelling visualizations using tools like Matplotlib and Tableau.
  • Model Deployment and Monitoring: Understand how to implement and maintain data models in a production environment, ensuring they deliver ongoing value.
  • Effective Communication: Learn how to present your findings to stakeholders clearly and persuasively, highlighting the implications of your analysis.

By the end of this course, you will have a solid understanding of data science principles and the practical skills necessary to analyze data effectively. Join us and begin your journey into the exciting world of data science, where your insights can drive impactful change across industries.

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Instructor

Dr Richards Michael

Data Engineer

  • 5.0
  • 1,500+ Students

Mentor Richards is a skilled Data Analytics professional with a Master's in Big Data Analytics from the University of Derby, UK. He has expertise in various Engineering Tech Stacks, contributing to process optimization and market enhancement for organizations. His strategic approach focuses on promoting business growth and efficiency. Additionally, Richards is committed to knowledge-sharing and continuous learning within the tech community, advancing data analytics and technology.

Review
Adenike Idowu
4.9

Empowering and Educative!

I am grateful for the invaluable resources that Vephla University provided throughout my studies. The platform’s comprehensive curriculum was a perfect fit for my diploma program, offering both essential knowledge and specialized skills needed in my field. The self-paced learning format was incredibly beneficial, especially during my hectic finals season— I could manage my time while diving deep into complex topics.

  • Helpful
  • Not helpful
Wivina Omolemen
4.9

Life-Changing Educational Journey!

As a recent graduate, I can confidently say that Vephla University played an instrumental role in my academic success. The platform offered a diverse range of courses that not only enriched my knowledge but also developed my practical skills. The interactive learning modules made difficult concepts easier to grasp, and I loved how I could learn at my own pace.

  • Helpful
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This course includes:

  • Job summit

  • Portfolio creation

  • Projects

  • Live classes

  • Full lifetime access

  • Diploma certification

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